Dr. Richard Gault is a Lecturer in the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on computer vision and deep learning applied to microscopy data, particularly in medicine, health, and life sciences. He leads a team developing novel methods for medical image analysis, including histopathology and digital pathology, with applications in cancer diagnosis and environmental science. He is actively involved in teaching, having received Excellence in Teaching awards from Queen's University Belfast in 2019 and 2022. His work bridges computational intelligence and healthcare, with notable contributions to AI-driven diagnostics, stain normalization in histopathology, and multimodal data fusion. Dr. Gault's research interests include ensemble learning, fuzzy systems, and generative models like diffusion networks. He supervises multiple PhD students and has mentored graduates now working in machine learning engineering and postdoctoral research. His team’s achievements include awards such as the 2023 Best Oral Presentation at the Pan Ireland Ophthalmology Day and a 2021 Best Paper Award from his school. Key contributions include the LymphoSight AI application for detecting lymphoid structures and HistoClean , open-source software for improving CNN development in histopathology. He has been recognized as a Senior Member of IEEE and a Fellow of the Higher Education Academy. His work is supported by grants such as the R5131ECI project on 3D quantifier approximation via 2D video analysis (2019–2025). He actively engages in academic activities, including conference organization and PhD external examinations across Europe.
Sebastiano Battiato is a Full Professor of Computer Science at the University of Catania's Department of Mathematics and Computer Science. He serves as Scientific Coordinator of the PhD Program in Computer Science and Deputy Rector for Strategic Planning and Information Systems at the University of Catania. As Director and Co-Founder of the International Computer Vision Summer School (ICVSS), he has significantly contributed to computer vision education globally. Education: Bachelor's degree in Computer Science (summa cum laude), University of Catania, 1995 Ph.D. in Computer Science and Applied Mathematics, University of Naples, 1999 Professor Battiato's research primarily focuses on Computer Vision, Imaging Technology, and Multimedia Forensics . His work spans from developing ISP algorithms for embedded devices to creating advanced techniques for image enhancement, coding, and forensic analysis. He has pioneered research in social media forensics, developing methods to determine if images have been processed through specific social platforms. His research has practical applications in assistive technologies, retail, digital marketing, and medical fields. His scholarly output shows a consistent focus on digital forensics and image processing, with an increasing emphasis on social media forensics in recent years. The research trajectory demonstrates progression from foundational image processing techniques to sophisticated forensic applications capable of addressing modern challenges like deepfakes and social media manipulation. Scientific Awards: 2017 PAMI Mark Everingham Prize for the series of annual ICVSS schools 2011 Best Associate Editor Award of IEEE Transactions on Circuits and Systems for Video Technology Professor Battiato has coordinated IPLab's participation in numerous large-scale research projects funded by national and international bodies as well as private companies. He has served as principal investigator on many international and national research projects, demonstrating strong leadership in securing research funding. His editorial work includes serving as associate editor for the SPIE Journal of Electronic Imaging and IET Image Processing Journal, and membership on several other editorial boards. As Director of IPLab research lab (http://iplab.dmi.unict.it), Professor Battiato leads a team focused on computer vision and digital forensics. The lab collaborates extensively with law enforcement agencies through iCTLAB, a university spinoff he founded that provides digital forensic services. IPLab is recognized for its contributions to image/video forensics, with techniques implemented in commercial forensic software like AMPED Authenticate.
Said Hamdioui serves as a full Professor in the Department of Computer Engineering within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. His research focuses on cutting-edge hardware architectures for neuromorphic computing and energy-efficient AI acceleration, with particular emphasis on memristor-based systems, emerging memory technologies, and fault-tolerant designs for edge applications. His research interests span Neuromorphic Computing , Memristor-Based Architectures , and Energy-Efficient AI Hardware , addressing critical challenges in hardware security, computation-in-memory, and reliable edge AI deployment. Recent work demonstrates significant advancements in RRAM/FeFET testing methodologies, spiking neural network implementations, and spin wave computing alternatives to traditional CMOS. His publications reveal strong trends toward real-world deployment of brain-inspired hardware with practical constraints like power efficiency, testability, and security. Award highlights include: DATE'20 Best Paper Award DFT'21 Outstanding Student Paper ETS 2021 Best Paper Award LATS 2018 & 2022 Best Paper Awards Professor Hamdioui actively contributes to the research community through editorial roles at IEEE Transactions on VLSI Systems , IEEE Design & Test , and Journal of Electronic Testing from 2017-2018. His leadership in multi-partner projects like CONVOLVE and NEUROKIT2E demonstrates strong industry-academia collaboration for edge AI solutions. Current work shows increasing focus on practical deployment challenges including in-field fault monitoring, security vulnerabilities in neuromorphic systems, and realistic brain simulation frameworks.
Thorsten Koch serves as Head of the Department of Applied Algorithmic Intelligence Methods within the Division of Mathematical Algorithmic Intelligence at Zuse Institute Berlin (ZIB). His research spans mathematical optimization, energy systems modeling, quantum computing applications, and scientometrics. Koch leads significant research projects including FAN (focusing on AI in scholarly communication), UNSEEN (energy scenarios), HPO-NAVI (research software visibility), and Multi-Energy Models for European Energy System Planning. Koch's research interests center on developing advanced optimization algorithms for complex systems, particularly in energy networks and scientific data analysis. His work bridges theoretical mathematics with practical applications in gas network optimization, wind farm design, portfolio management, and quantum computing. He has pioneered methods for large-scale mixed-integer programming, scenario generation, and the integration of machine learning with traditional optimization techniques. His recent publications demonstrate growing emphasis on quantum optimization, scientometrics, and the application of AI to scientific communication infrastructure. His publication trends reveal a strategic expansion from traditional mathematical optimization into quantum computing applications and scientific data infrastructure. Recent work shows increasing collaboration across disciplines - connecting energy systems analysis with financial modeling, integrating machine learning with optimization solvers, and applying computational methods to scientometrics. The 15 most recent articles highlight three major thrusts: quantum optimization (33%), energy systems modeling (27%), and scientific data infrastructure (40%), reflecting his leadership in both theoretical algorithm development and practical implementation for societal challenges. Koch actively contributes to research infrastructure through leadership roles in projects like KOBV (Berlin-Brandenburg Cooperative Library Network), HDC (Humanities Data Centre), and CIB (future library networks). His work on the DeepGreen initiative focuses on establishing legally secure workflows for implementing open-access components in scientific publication licensing agreements, demonstrating his commitment to open science principles and research data management.
Andrew Pavlo is a Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research focuses on database management systems, particularly in the areas of transaction processing, in-memory databases, and self-driving database systems. He leads a productive research group that has published extensively in top database venues including VLDB, SIGMOD, and CIDR. Pavlo's research interests span database management systems, transaction processing, in-memory databases, non-volatile memory databases, and self-driving database systems. His work often bridges theoretical database concepts with practical system implementation, focusing on performance optimization, query processing, and system architecture. Recent work has explored machine learning applications for database tuning, novel storage techniques, and innovative approaches to transaction processing. An analysis of his recent publications reveals a strong focus on self-driving database systems, with significant work on the Database Gym framework for training machine learning models to optimize database performance. His research also examines columnar storage formats, transaction scheduling, and novel approaches to user-defined function optimization. The work demonstrates a consistent trajectory toward making database systems more autonomous and efficient through a combination of systems techniques and machine learning. Pavlo has been instrumental in mentoring numerous PhD students who have become active contributors to the database research community. His research has been supported by significant grants that have enabled the development of innovative database technologies and frameworks. His research group operates within CMU's vibrant database ecosystem, collaborating with other researchers on projects related to database systems, storage engines, and query processing frameworks. The group maintains close connections with industry partners to ensure practical relevance of their research contributions.
Adam J. Aviv is an Associate Professor of Computer Science at The George Washington University, leading the George Washington University Usable Security and Privacy Lab (gwusec) . His work focuses on computer security, privacy, and usable security , with a particular emphasis on user behavior, authentication systems, and mobile/web security. University: The George Washington University Academic Rank: Associate Professor Email: aaviv@gwu.edu Research Interests: His research investigates how users interact with security and privacy systems, including studies on: Biometric and mobile authentication Password manager usability Generative AI risk perception Online proctoring and institutional decisions Data breach responses Privacy labels and user trust Recent Publications (2024–2025) span venues like USENIX Security, IEEE S&P, ACM CHI, and PoPETs, covering topics such as: Wearable-based contact tracing in low-resource settings WhatsApp mod security perceptions Password manager issues Privacy label accuracy Grants & Awards: Recipient of the OVPR Research Mentorship Award for his work with students. Currently holds NSF grants for collaborative cybersecurity research and travel funding for Privacy Enhancing Technology Symposium. Teaching: Offers Intro to Usable Security and Privacy (CSCI 4533/6533) in Fall 2025, with students engaging in: Secure messaging studies Interview and survey methodology Full research projects with ethics reports Laboratory: The gwusec lab focuses on user-centered security and privacy research , collaborating with institutions like Tel Aviv University and University of Haifa.
Dr. Tim Conrad is a researcher at the Zuse Institute Berlin in the Visual and data-centric computing department under the Mathematics of Complex Systems division. He leads projects at the intersection of computational biology, AI, and medical data analysis. Projects: Geometric Learning for Single-Cell RNA Velocity Modeling, MODAL MedLab, Sparse Compressed Sensing in -Omics Data, BIFOLD (Big Data and Machine Learning) Research Networks: Affiliated with MATH+ and MODAL Research Campus His research focuses on applying machine learning , network optimization , and sparse data analysis to biological and medical challenges including disease modeling, microbiome dynamics, and ECG classification. Recent work explores hybrid PDE-ODE epidemic models and federated learning in healthcare. 2023-2025 publications highlight trends in AI for biological networks , temporal community detection , and medical signal processing . He co-authored studies on SARS-CoV-2 simulations, proteomics feature selection, and multi-label ECG analysis. His 2004 doctoral thesis at Monash University laid foundations for later work in metabolic pathway analysis. Awarded as a Zuse Fellow , he contributes to open science initiatives like FAIR data sharing . Collaborations span institutions including Freie Universität Berlin and Charité in medical informatics and clinical applications.
Dr. Martin Kleppmann is an Associate Professor at the University of Cambridge, specializing in local-first software and security protocols . He leads research in distributed systems, focusing on decentralized architectures, collaborative editing tools, and cryptographic methods. As a key contributor to the Automerge open-source project, he bridges academic innovation with real-world applications. Formerly a research fellow at TU Munich (2022–2023) and Cambridge (2015–2022), he has also worked as a software engineer and startup founder, including LinkedIn (acquired 2012). Research Interests span Distributed Systems Security , Conflict-Free Replicated Data Types (CRDTs) , Collaborative Software , and Cryptography . His work addresses challenges in decentralized social networks, privacy-preserving protocols, and efficient data synchronization. Recent projects include Kintsugi (end-to-end encrypted key recovery) and Pudding (private user discovery for anonymity networks). Publications emphasize Collaborative text editing (2025: Eg-walker, 2023: The Art of the Fugue) CRDTs for JSON and trees (2021, 2017) Privacy in decentralized systems (2024: Pudding, 2025: Emission Impossible) Scientific Awards include Gilles Muller Best Artifact Award (EuroSys 2025) Distinguished Paper & Artifact Awards (OOPSLA 2017) Best Presentation Awards (Security Protocols Workshop 2018, PaPoC 2025)
Dr. Todd D. Murphey is a Professor of Mechanical Engineering at Northwestern University's Robert R. McCormick School of Engineering and Applied Science. He serves as Director of Transformative Research and Director of the Master of Science in Robotics Program at Northwestern, leading initiatives in computational dynamics, control systems, and robotics. His work bridges engineering, neuroscience, and biomedical applications, with a focus on developing systems that interact effectively with humans and their environments. Dr. Murphey received his Ph.D. in Control and Dynamical Systems from the California Institute of Technology in 2002, with a thesis titled "Control of Multiple Model Systems." Prior to that, he earned a B.S. in Mathematics, summa cum laude, from the University of Arizona in 1997. Dr. Murphey's research centers on computational methods in dynamics and control, with applications spanning neuroscience, health science, robotics, and automation. His work in the Interactive & Emergent Autonomy Lab focuses on computational models of embedded control, biomechanical simulation, dynamic exploration, and hybrid control. The group develops mathematical approaches that lead to orders of magnitude improvement in computational efficiency for real-time implementation. Key application areas include assistive exoskeleton control, stabilization of energy networks, bio-inspired active sensing, entertainment robots, robotic exploration, and software-enabled stroke rehabilitation. Analysis of Dr. Murphey's recent publications reveals a strong emphasis on human-swarm interaction, algorithmic matter, and control of cyber-physical systems in uncertain environments. His work increasingly integrates information theory with physical systems, exploring how both autonomous and biological systems interact with environments to learn and improve behaviors. Recent trends show growing applications in rehabilitation technology, with particular focus on human-machine interaction in biomedical devices and embodied intelligence. Dr. Murphey has received numerous honors and awards for his contributions to robotics and engineering: Named Director of Transformative Research at Northwestern University (2025) Appointed IEEE Robotics and Automation Society Vice President of Publication Activities (2022) Co-recipient of Best Paper Award for IEEE Transactions on Robotics (2020) Appointed to Air Force Scientific Advisory Board (2019) Recipient of ABB Best Student Paper Award for CPL-SLAM research (2019) Cole-Higgins Award from Northwestern Engineering (2015) Dr. Murphey has supervised numerous graduate students including Taosha Fan, Giorgos Mamakoukas, and Ian Abraham, with research spanning robotic exploration using electrosense and mechanical contact, human-in-the-loop control, and shared control for rehabilitation devices. His lab has secured significant funding from the National Science Foundation, DARPA, and industry partners including Siemens and Ekso Bionics, supporting research in algorithmic matter, emergent behavior, and human-swarm collaboration. The Interactive & Emergent Autonomy Lab, led by Dr. Murphey, investigates how both autonomous systems and biological systems interact with their environments to learn and improve behaviors. Current projects include active learning and data-driven control, active perception in human-swarm collaboration, algorithmic matter and emergent computation, control for nonlinear and hybrid systems, cyber physical systems in uncertain environments, harmonious navigation in human crowds, information maximizing clinical diagnostics, reactive learning in underwater exploration, robot-assisted rehabilitation, and software-enabled biomedical devices. The lab collaborates with researchers across Northwestern and institutions including Georgia Tech, MIT, and industry partners.
Ludovic Räss is a computational geoscientist at the University of Lausanne and lecturer at ETH Zurich's Glaciology Lab. His research intersects high-performance computing (HPC), geophysics, and applied mathematics, with specialization in GPU-accelerated scientific computing and supercomputing applications. He leads the GPU4GEO initiative developing multi-physics solvers and pioneers differentiable modeling techniques for geophysical simulations using Julia. Research focuses include: Portable HPC software development Ice dynamics and porous media deformation GPU-optimized computational methods Scalable simulation architectures Differentiable programming for geophysics He designed and teaches Solving partial differential equations in parallel on GPUs at ETH Zurich, providing hands-on training in GPU programming and Julia-based scientific computing. Contributes significantly to Julia's open-source ecosystem through JuliaGPU and JuliaParallel projects.
Facundo M. Fernandez is a Regents' Professor and Vasser-Woolley Chair in Bioanalytical Chemistry at the Georgia Institute of Technology, where he leads the Fernandez Research Group within the School of Chemistry and Biochemistry in the College of Sciences. His research spans multiple cutting-edge areas of analytical chemistry with significant applications in medicine, forensics, and basic science. Dr. Fernandez earned his M.Sc. in Chemistry (1996) and Ph.D. in Analytical Spectrometry/Metallomics (1999) from the Facultad de Ciencias Exactas y Naturales at Buenos Aires University, Argentina. His research program focuses on Bioanalytical Mass Spectrometry with particular emphasis on Ambient Sampling/Ionization & Molecular Imaging, Ion Mobility Spectrometry, Metabolomics, and Pharmaceutical Forensics. His work has pioneered new approaches in ambient ionization techniques that enable direct analysis of complex samples without extensive preparation. His recent publications reveal a strong trend toward spatial metabolomics, particularly in traumatic brain injury and ovarian cancer research, with increasing integration of machine learning approaches for data analysis. His work also extends to pharmaceutical quality control, exercise physiology through the MoTrPAC consortium, and prebiotic chemistry investigations. The interdisciplinary nature of his research is evident in collaborations across Georgia Tech's campus and with external institutions. NSF CAREER Award (2007) 3M Non-tenured Faculty Award (2008) CETL/BP Junior Faculty Teaching Excellence Award (2009) Ron A. Hites Award for Outstanding Research Publication (2010) Sigma Xi (GT Chapter) Best Faculty Paper Award (2010) Vasser-Wooley Faculty Fellow (2012) Dr. Fernandez has secured significant funding for his research, including NSF CAREER support, and leads projects related to metabolomics for ovarian cancer detection, pharmaceutical forensics through the CODFIN network, and participation in the large-scale Molecular Transducers of Physical Activity Consortium (MoTrPAC). His laboratory develops advanced instrumentation for mass spectrometry applications and maintains strong collaborations with the Integrated Cancer Research Center, the College of Computing, and the Center for Chemical Evolution at Georgia Tech.
Giuseppe Santucci is an Associate Professor at the Department of Computer, Control and Management Engineering Antonio Ruberti at Sapienza University of Rome. He teaches courses on Fundamentals of Computer Science, Software Engineering, and Visual Analytics. His office is located in Room B218 at Via Ariosto 25, Rome, and his contact email is santucci@diag.uniroma1.it. Dr. Santucci's research focuses on Visual Analytics, Information Visualization, Human-Computer Interaction, and Information Retrieval. His work spans theoretical aspects of visual query languages for semantic models to practical applications in visual analytics for cybersecurity, cryptocurrencies, and deep learning explainability. He has published over 130 articles in international journals and conferences, demonstrating his significant contributions to these fields. His recent publications show a strong trend toward applying visual analytics to increasingly complex domains including cybersecurity, cryptocurrencies, and explainable AI. The work demonstrates an evolution from theoretical foundations of visual query systems to practical applications that help users understand complex data and systems. His research bridges the gap between theoretical computer science and practical user-centered solutions. Dr. Santucci has received notable recognition including: IEEE VizSec 2018 Best Paper Award Human-Computer Interaction Cybersecurity Awards 2018 He actively mentors students through thesis projects focused on information visualization and visual analytics. His PROMISE project provides a framework for students to engage in cutting-edge research in information retrieval and visual analytics. He has supervised work on topics including visual evaluation techniques, visual mappings optimization, and user studies for Infovis systems. Dr. Santucci leads the A.WA.RE (Advanced Visualization & Visual Analytics REsearch) group at Sapienza University. This group conducts research on visual analytics tools for information retrieval evaluation, cybersecurity analysis, and deep learning explainability. Their work includes developing frameworks like CryptoComparator for cryptocurrency analysis and BUCEPHALUS for cybersecurity platform analysis.
Michael D. Ernst is a Professor in the Computer Science & Engineering department at the University of Washington's College of Engineering. His research aims to make software more reliable, more secure, and easier (and more fun!) to produce. Previously, he was a tenured professor at MIT and a researcher at Microsoft Research. Ernst's primary technical interests are in software engineering, programming languages, type theory, security, program analysis, bug prediction, testing, and verification. His research combines strong theoretical foundations with realistic experimentation, with an eye to changing the way that software developers work. He focuses particularly on programmer productivity and developing practical tools that can be integrated into developers' workflows. Analysis of his recent publications (2018-2025) reveals a continued focus on verification techniques, program analysis, and testing methodologies. His work spans from theoretical foundations of type systems to practical applications of NLP for test generation and LLMs for test oracle creation. A consistent theme is developing lightweight, modular approaches that can be practically applied in real-world development environments. Scientific Awards: ACM Fellow (2014) John Backus Award (2009) NSF CAREER Award (2002) ACM SIGSOFT Impact Paper Award (2013) 8 ACM Distinguished Paper Awards across multiple conferences ECOOP 2011 Best Paper Award Microsoft Academic Search ranked #2 in software engineering research (2013) Ernst has received significant research funding including the NSF CAREER Award, supporting his work on program analysis and verification techniques. His research combines theoretical rigor with practical impact, often resulting in tools that are adopted by the software engineering community. He actively collaborates with researchers across institutions and has served in leadership roles for major conferences in programming languages and software engineering. His research group develops practical tools that address real challenges in software development, with a focus on making verification and analysis techniques more accessible to working developers. Current projects include applying machine learning techniques to software engineering problems while maintaining strong theoretical foundations.
Saugata Ghose is an Assistant Professor in the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign (UIUC), with affiliate appointments in the Coordinated Science Laboratory and the Department of Electrical and Computer Engineering. His research focuses on data-centric computing, processing-in-memory architectures, memory systems, and hardware-software co-design. He holds a Ph.D. and M.S. in Computer Engineering from Cornell University and dual B.S. degrees in Computer Engineering and Computer Science from SUNY Binghamton. His academic positions include roles at Carnegie Mellon University (2016–2020) and postdoctoral research at CMU (2014–2016). Ghose has received notable awards such as the 2024 HPCA Hall of Fame, 2023 Intel Rising Star Faculty Award, and the 2019 CMU Wimmer Faculty Fellowship. His work has been supported by grants from NSF, Samsung, and Sandia National Laboratories. Research Interests: His group (ARCANA) explores data-centric architectures, processing-in-memory (PIM), and emerging memory technologies. Key areas include architectures for smart cities, autonomous systems, and genomics. He teaches courses on computer architecture and systems organization. Awards: HPCA Hall of Fame (2024) Intel Rising Star Faculty Award (2023) CMU Wimmer Faculty Fellow (2019) Cornell ECE Teaching Assistant Award (2013) Grants & Projects: NSF $2M for semiconductor advancements Samsung/Sandia grants for PIM programming models UIUC/ZJU DREMES collaboration on neuromorphic PIM Labs/Teams: Leads the ARCANA Research Group, focusing on reimagining computing around new applications. Collaborates with ASAP and HYBRID centers for co-design tools and neuromorphic architectures.
Jose Israel Rodriguez is an Assistant Professor in the Department of Mathematics at the University of Wisconsin-Madison, with additional affiliations in the Department of Electrical & Computer Engineering and the Institute for Foundations of Data Science. He joined UW Madison in Fall 2020 after completing postdoctoral positions at the University of Chicago (with Lek-Heng Lim) and Notre Dame (with Jonathan Hauenstein). Rodriguez earned his PhD in 2014 from UC Berkeley under the supervision of Bernd Sturmfels. His research focuses on applied algebraic geometry and algebraic methods for statistics, with particular interests in nonlinear algebra and nonlinear eigenvalue problems, algebraic statistics and nearest point problems, and applications of monodromy and Galois groups. Rodriguez has made significant contributions to numerical algebraic geometry, particularly in solving polynomial systems, maximum likelihood estimation, and Euclidean distance degree calculations. His work bridges theoretical mathematics with practical computational methods. Rodriguez's recent publications demonstrate a strong trend toward developing numerical methods for solving complex algebraic problems with applications in statistics, optimization, and engineering. His research shows increasing sophistication in handling decomposable systems, multiparameter eigenvalue problems, and braid group computations, often implementing these methods in software tools like Macaulay2. His work connects abstract algebraic geometry with concrete computational approaches. NSF Postdoctoral Fellow Provost's Postdoctoral Scholar Rodriguez currently advises PhD students Julia Lindberg (expected graduation May 2022, joint with B. Lesieutre) and Zinan Wang. He has organized numerous seminars and conferences including SIAM_SAGA, Algebra in Statistics and Computation Seminar, and Applied Algebra Seminar. His research has been supported by various grants that enable his work in numerical algebraic geometry and its applications. Rodriguez is actively involved in the algebraic geometry and statistics communities, organizing several seminars and minisymposia at major conferences. He has developed several software tools including implementations for decomposable sparse polynomial systems, multiregeneration, algebraic optimization, Galois groups, and maximum likelihood obstruction functions. His work connects theoretical mathematics with practical computational applications across various domains.
